
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from sklearn.decomposition import PCA
from sklearn.manifold import MDS
from sklearn.metrics.pairwise import manhattan_distances,pairwise_distances


task1_data = pd.read_csv('IE582_Fall21_HW2_q1_data.csv')
print('Data shape: ',task1_data.shape)
print(task1_data.head())

Data shape:  (198, 3)
         X1        X2 class
0  0.569483  0.822003     a
1  0.411469  0.911424     a
2  0.417385 -0.908730     a
3 -0.791828  0.610745     a
4 -0.806777 -0.590857     a


task1_data['class'].value_counts()

a    99
b    99
Name: class, dtype: int64


fig = px.scatter(task1_data, x="X1", y="X2", color="class", symbol="class")
fig.update_layout(yaxis=dict(scaleanchor="x"))
fig.update_layout(
    width = 500,
    height = 500,
    title = "Scatter plot of the feature X1 vs X2 and the class info."
)
fig.show()


pca = PCA(n_components=1)
pca.fit(task1_data[['X1','X2']])

PCA(n_components=1)


task1_pca = pd.DataFrame(pca.transform(task1_data[['X1','X2']]),columns=['feature_pca'],index=task1_data.index)
print('Data shape: ',task1_pca.shape)
print(task1_pca.head())

Data shape:  (198, 1)
   feature_pca
0     0.722069
1     0.811347
2    -1.008801
3     0.509578
4    -0.692037


#join pca results with class info
task1_pca = pd.merge(task1_pca,task1_data[['class']],how='left',right_index=True, left_index=True)

#plot the feature
fig = px.scatter(task1_pca.reset_index().rename(columns={'index':'observation number'}), x="observation number", y="feature_pca",color="class", symbol="class")
fig.update_layout(
    title = "Scatter plot of the feature obtained from PCA and observation number")
fig.show()


mds_euc = MDS(n_components=1,dissimilarity='euclidean',random_state=42)
task1_mds_euc = task1_data[['X1','X2']].copy()
task1_mds_euc = pd.DataFrame(mds_euc.fit_transform(task1_mds_euc),columns=['feature_mds_euc'],index=task1_data.index)
print('Data shape: ',task1_mds_euc.shape)
print(task1_mds_euc.head())

Data shape:  (198, 1)
   feature_mds_euc
0        -0.640953
1        -0.690699
2         1.138472
3        -0.199635
4         0.651335


#join mds results with class info
task1_mds_euc = pd.merge(task1_mds_euc,task1_data[['class']],how='left',right_index=True, left_index=True)


fig = px.scatter(task1_mds_euc.reset_index().rename(columns={'index':'observation number'}), x="observation number", y="feature_mds_euc",color="class", symbol="class")
fig.update_layout(
    title = "Scatter plot of the feature obtained from MDS (using Euclidian distance) and observation number")
fig.show()


task1_mds_man = manhattan_distances(task1_data[['X1','X2']]) #calculate distance matrix
mds_man = MDS(n_components=1,dissimilarity='precomputed',random_state=42)
task1_mds_man = pd.DataFrame(mds_man.fit_transform(task1_mds_man),columns=['feature_mds_man'],index=task1_data.index)
print('Data shape: ',task1_mds_man.shape)
print(task1_mds_man.head())

Data shape:  (198, 1)
   feature_mds_man
0        -0.682587
1        -0.755814
2         1.391906
3        -0.207478
4         0.840368


#join mds results with class info
task1_mds_man = pd.merge(task1_mds_man,task1_data[['class']],how='left',right_index=True, left_index=True)

fig = px.scatter(task1_mds_man.reset_index().rename(columns={'index':'observation number'}), x="observation number", y="feature_mds_man",color="class", symbol="class")
fig.update_layout(
    title = "Scatter plot of the feature obtained from MDS (using Manhattan distance) and observation number")
fig.show()


task1_data_partd = task1_data[['X1','X2']].copy()
task1_data_partd['X3'] = task1_data['X1']*task1_data['X1']
task1_data_partd['X4'] = task1_data['X2']*task1_data['X2']
task1_data_partd['X5'] = task1_data['X1']*task1_data['X2']


pca_partd = PCA(n_components=1)
pca_partd.fit(task1_data_partd)

PCA(n_components=1)


task1_data_partd_pca1 = pd.DataFrame(pca_partd.transform(task1_data_partd),columns=['feature_pca'],index=task1_data.index)
print('Data shape: ',task1_data_partd_pca1.shape)
print(task1_data_partd_pca1.head())

Data shape:  (198, 1)
   feature_pca
0    -0.139714
1     0.051931
2    -1.190015
3    -0.581394
4    -1.534138


#join pca results with class info
task1_data_partd_pca1 = pd.merge(task1_data_partd_pca1,task1_data[['class']],how='left',right_index=True, left_index=True)

#plot the feature
fig = px.scatter(task1_data_partd_pca1.reset_index().rename(columns={'index':'observation number'}), x="observation number", y="feature_pca",color="class", symbol="class")
fig.update_layout(
    title = "Scatter plot of the feature obtained from PCA and observation number")
fig.show()


print('Eigenvalues:')
print(pca_partd.explained_variance_)

Eigenvalues:
[2.49086525]


pca_partd2 = PCA()
pca_partd2.fit(task1_data_partd)
task1_data_partd_pca2 = pd.DataFrame(pca_partd2.transform(task1_data_partd),index=task1_data.index)
print('Data shape: ',task1_data_partd_pca2.shape)
task1_data_partd_pca2.columns=['F_'+str(x) for x in range(1,task1_data_partd_pca2.shape[1]+1)]
print(task1_data_partd_pca2.head())

Data shape:  (198, 5)
        F_1       F_2       F_3       F_4       F_5
0 -0.139714 -0.300223 -1.253871 -0.832173  0.486491
1  0.051931 -0.300438 -1.260008 -0.734355  0.278286
2 -1.190015 -0.807435  0.007729 -0.650498 -0.406087
3 -0.581394 -0.550799 -1.346118  0.704651 -0.112335
4 -1.534138  0.355528 -0.830090  0.126772 -0.307266


#join pca results with class info
task1_data_partd_pca2 = pd.merge(task1_data_partd_pca2,task1_data[['class']],how='left',right_index=True, left_index=True)

#plot the features
fig = px.scatter(task1_data_partd_pca2.reset_index().rename(columns={'index':'observation number'}), x="observation number", y="F_1",color="class", symbol="class")
fig.update_layout(
    title = "Scatter plot of the feature obtained (F_1) from PCA and observation number")
fig.show()

fig = px.scatter(task1_data_partd_pca2.reset_index().rename(columns={'index':'observation number'}), x="observation number", y="F_2",color="class", symbol="class")
fig.update_layout(
    title = "Scatter plot of the feature obtained (F_2) from PCA and observation number")
fig.show()

fig = px.scatter(task1_data_partd_pca2.reset_index().rename(columns={'index':'observation number'}), x="observation number", y="F_3",color="class", symbol="class")
fig.update_layout(
    title = "Scatter plot of the feature obtained (F_3) from PCA and observation number")
fig.show()

fig = px.scatter(task1_data_partd_pca2.reset_index().rename(columns={'index':'observation number'}), x="observation number", y="F_4",color="class", symbol="class")
fig.update_layout(
    title = "Scatter plot of the feature obtained (F_4) from PCA and observation number")
fig.show()

fig = px.scatter(task1_data_partd_pca2.reset_index().rename(columns={'index':'observation number'}), x="observation number", y="F_5",color="class", symbol="class")
fig.update_layout(
    title = "Scatter plot of the feature obtained (F_5) from PCA and observation number")
fig.show()


print('Eigenvalues:')
print(pca_partd2.explained_variance_)
print('They refer to the variance covered by components')

Eigenvalues:
[2.49086525 1.44256642 1.28808119 0.54559809 0.30619998]
They refer to the variance covered by components


task2_distances = pd.read_excel('ilmesafe.xls',skiprows=[0,1])
task2_distances.fillna(0,inplace=True)
task2_distances.head()


#check if the data is symmetric
print('Max difference between data and its transpose: ',(np.array(task2_distances.iloc[:,2:])-np.array(task2_distances.iloc[:,2:]).T).max())
print('The data is not symmetric')

Max difference between data and its transpose:  87.0
The data is not symmetric


#Make the data symmetric by equating its upper triangle to its lower one
task2_distances.iloc[:,2:] = np.tril(np.array(task2_distances.iloc[:,2:])).T+np.tril(np.array(task2_distances.iloc[:,2:]))


#check if the data is symmetric
print('Max difference between data and its transpose: ',(np.array(task2_distances.iloc[:,2:])-np.array(task2_distances.iloc[:,2:]).T).max())
print('The data is symmetric now')

Max difference between data and its transpose:  0.0
The data is symmetric now


task2_distances


task2_mds = MDS(n_components=2,dissimilarity='precomputed',random_state=42)
task2_distances_mds = pd.DataFrame(task2_mds.fit_transform(task2_distances.iloc[:,2:]),columns=['X1','X2'],index=task2_distances['İL ADI'])
print('Data shape: ',task2_distances_mds.shape)
print(task2_distances_mds.head())

Data shape:  (81, 2)
                        X1          X2
İL ADI                                
ADANA           371.882973   62.741623
ADIYAMAN        335.693512  402.428156
AFYONKARAHİSAR  132.285096 -476.774865
AĞRI           -150.942284  799.351470
AMASYA         -199.224838   31.278456


task2_distances_mds1 = task2_distances_mds.reset_index()

#plot the feature
fig = px.scatter(task2_distances_mds1, x="X2", y="X1",text="İL ADI")
fig.update_layout(
    title = "MDS on Euclidian distances between cities of Turkey")
fig.show()


task2_distances_mds2 = task2_distances_mds.reset_index()
task2_distances_mds2['X1'] = task2_distances_mds2['X1']*-1
task2_distances_mds2['X2'] = task2_distances_mds2['X2']
#plot the feature
fig = px.scatter(task2_distances_mds2, x="X2", y="X1",text="İL ADI")
fig.update_layout(
    title = "MDS on Euclidian distances between cities of Turkey")
fig.show()


task3_x = pd.read_csv('uWaveGestureLibrary_X_TRAIN.csv', sep=';', header=None)
print('Data shape: ',task3_x.shape)
task3_x.columns = ['class']+['x'+str(x) for x in range(1,task3_x.shape[1])]
task3_x['class']=task3_x['class'].astype(int)
task3_x.head()

Data shape:  (896, 316)


print('Number of null observations: ',task3_x.isnull().any().sum())
print('Class counts: ')
print(task3_x['class'].value_counts().sort_index())

Number of null observations:  0
Class counts: 
1    122
2    108
3    106
4    110
5    127
6    111
7    112
8    100
Name: class, dtype: int64


task3_y = pd.read_csv('uWaveGestureLibrary_Y_TRAIN.csv', sep=';', header=None)
print('Data shape: ',task3_y.shape)
task3_y.columns = ['class']+['y'+str(x) for x in range(1,task3_y.shape[1])]
task3_y['class']=task3_y['class'].astype(int)
task3_y.head()

Data shape:  (896, 316)


print('Number of null observations: ',task3_y.isnull().any().sum())
print('Class counts: ')
print(task3_y['class'].value_counts().sort_index())

Number of null observations:  0
Class counts: 
1    122
2    108
3    106
4    110
5    127
6    111
7    112
8    100
Name: class, dtype: int64


task3_z = pd.read_csv('uWaveGestureLibrary_Z_TRAIN.csv', sep=';', header=None)
print('Data shape: ',task3_z.shape)
task3_z.columns = ['class']+['z'+str(x) for x in range(1,task3_z.shape[1])]
task3_z['class']=task3_z['class'].astype(int)
task3_z.head()

Data shape:  (896, 316)


print('Number of null observations: ',task3_z.isnull().any().sum())
print('Class counts: ')
print(task3_z['class'].value_counts().sort_index())

Number of null observations:  0
Class counts: 
1    122
2    108
3    106
4    110
5    127
6    111
7    112
8    100
Name: class, dtype: int64


for i in range(1,9):
    ii = task3_x[task3_x['class']==i].head(1).index[0]
    X_list = task3_x[task3_x['class']==i].head(1).iloc[0,1:].values
    Y_list = task3_y[task3_y.index==ii].iloc[0,1:].values
    Z_list = task3_z[task3_z.index==ii].iloc[0,1:].values

    fig = go.Figure(data=[go.Scatter3d(x=X_list, y=Y_list, z=Z_list,
                                       mode='markers')])
    fig.update_layout(
        title = "3D scatter plot of observation "+str(ii)+" belonging to the class "+str(i))
    fig.show()


task3_x_cumsum = task3_x.copy()
task3_x_cumsum.iloc[:,1:] = task3_x_cumsum.iloc[:,1:].cumsum(axis=1)#velocity
task3_x_cumsum.iloc[:,1:] = task3_x_cumsum.iloc[:,1:].cumsum(axis=1)#position
task3_y_cumsum = task3_y.copy()
task3_y_cumsum.iloc[:,1:] = task3_y_cumsum.iloc[:,1:].cumsum(axis=1)#velocity
task3_y_cumsum.iloc[:,1:] = task3_y_cumsum.iloc[:,1:].cumsum(axis=1)#position
task3_z_cumsum = task3_z.copy()
task3_z_cumsum.iloc[:,1:] = task3_z_cumsum.iloc[:,1:].cumsum(axis=1)#velocity
task3_z_cumsum.iloc[:,1:] = task3_z_cumsum.iloc[:,1:].cumsum(axis=1)#position


for i in range(1,9):
    ii = task3_x_cumsum[task3_x_cumsum['class']==i].head(1).index[0]
    X_list = task3_x_cumsum[task3_x_cumsum.index==ii].head(1).iloc[0,1:].values
    Y_list = task3_y_cumsum[task3_y_cumsum.index==ii].iloc[0,1:].values
    Z_list = task3_z_cumsum[task3_z_cumsum.index==ii].iloc[0,1:].values

    fig = go.Figure(data=[go.Scatter3d(x=X_list, y=Y_list, z=Z_list,
                                       mode='markers')])
    fig.update_layout(
        title = "3D scatter plot of observation "+str(ii)+" belonging to the class "+str(i))
    fig.show()


concatenated_series = pd.merge(task3_x_cumsum,task3_y_cumsum.drop('class',axis=1),how='left',left_index=True,right_index=True)
concatenated_series = pd.merge(concatenated_series,task3_z_cumsum.drop('class',axis=1),how='left',left_index=True,right_index=True)
print('Data shape: ',concatenated_series.shape)
concatenated_series.head()

Data shape:  (896, 946)


def PCA_and_Plot(i):
    
    print('CLASS : '+str(i))
    concatenated_series_c = concatenated_series[concatenated_series['class']==i].drop('class',axis=1)
    
    pca_c = PCA()
    pca_c.fit(concatenated_series_c)
    concatenated_series_c = pd.DataFrame(pca_c.transform(concatenated_series_c),index=concatenated_series_c.index)

    print('First component covers ',pca_c.explained_variance_ratio_[0]*100,'% of variance')
    print('Second component covers ',pca_c.explained_variance_ratio_[1]*100,'% of variance')

    first_component = pca_c.components_[0]
    second_component = pca_c.components_[1]
    x_axis = [x for x in range(1,pca_c.components_[1].shape[0]+1)]

    fig = go.Figure()
    fig.add_trace(go.Scatter(x=x_axis, y=first_component,
                        mode='lines',
                        name='First component'))
    fig.add_trace(go.Scatter(x=x_axis, y=second_component,
                        mode='lines',
                        name='Second component'))

    fig.update_layout(
    title = "Plot of first and second eigenvectors as time series of class :"+str(i))

    fig.show()


PCA_and_Plot(1)

CLASS : 1
First component covers  48.70273363671071 % of variance
Second component covers  25.640331744537665 % of variance


PCA_and_Plot(2)

CLASS : 2
First component covers  54.022708715041375 % of variance
Second component covers  25.443796949670826 % of variance


PCA_and_Plot(3)

CLASS : 3
First component covers  48.96345432283971 % of variance
Second component covers  39.26233452309135 % of variance


PCA_and_Plot(4)

CLASS : 4
First component covers  55.02840197323645 % of variance
Second component covers  35.49233237780508 % of variance


PCA_and_Plot(5)

CLASS : 5
First component covers  76.60314552721601 % of variance
Second component covers  18.07344539015742 % of variance


PCA_and_Plot(6)

CLASS : 6
First component covers  56.134555333254866 % of variance
Second component covers  33.48913312646308 % of variance


PCA_and_Plot(7)

CLASS : 7
First component covers  69.94502789924606 % of variance
Second component covers  18.350309804290436 % of variance


PCA_and_Plot(8)

CLASS : 8
First component covers  52.40604112460815 % of variance
Second component covers  31.72351997403757 % of variance

	İL PLAKA NO	İL ADI	ADANA	ADIYAMAN	AFYONKARAHİSAR	AĞRI	AMASYA	ANKARA	ANTALYA	ARTVİN	...	BATMAN	ŞIRNAK	BARTIN	ARDAHAN	IĞDIR	YALOVA	KARABÜK	KİLİS	OSMANİYE	DÜZCE
0	1	ADANA	0.0	335.0	575.0	966.0	603.0	567.0	535.0	1035.0	...	621.0	709.0	782.0	1042.0	1066.0	899.0	714.0	246.0	87.0	735.0
1	2	ADIYAMAN	335.0	0.0	910.0	648.0	632.0	814.0	870.0	751.0	...	303.0	471.0	1023.0	758.0	748.0	1147.0	955.0	210.0	248.0	976.0
2	3	AFYONKARAHİSAR	575.0	910.0	0.0	1318.0	597.0	300.0	290.0	1243.0	...	1196.0	1284.0	515.0	1351.0	1461.0	338.0	447.0	821.0	662.0	375.0
3	4	AĞRI	966.0	648.0	1318.0	0.0	738.0	1141.0	1428.0	396.0	...	369.0	430.0	1175.0	310.0	143.0	1363.0	1106.0	814.0	879.0	1192.0
4	5	AMASYA	603.0	632.0	597.0	736.0	0.0	413.0	825.0	695.0	...	796.0	982.0	437.0	783.0	881.0	625.0	368.0	639.0	608.0	454.0

	İL PLAKA NO	İL ADI	ADANA	ADIYAMAN	AFYONKARAHİSAR	AĞRI	AMASYA	ANKARA	ANTALYA	ARTVİN	...	BATMAN	ŞIRNAK	BARTIN	ARDAHAN	IĞDIR	YALOVA	KARABÜK	KİLİS	OSMANİYE	DÜZCE
0	1	ADANA	0.0	335.0	575.0	966.0	603.0	492.0	535.0	1035.0	...	621.0	709.0	782.0	1042.0	1069.0	899.0	714.0	246.0	87.0	735.0
1	2	ADIYAMAN	335.0	0.0	910.0	648.0	632.0	742.0	870.0	751.0	...	303.0	471.0	1028.0	758.0	751.0	1152.0	960.0	210.0	248.0	981.0
2	3	AFYONKARAHİSAR	575.0	910.0	0.0	1318.0	597.0	256.0	291.0	1243.0	...	1196.0	1284.0	515.0	1351.0	1429.0	338.0	447.0	821.0	662.0	375.0
3	4	AĞRI	966.0	648.0	1318.0	0.0	736.0	1054.0	1428.0	396.0	...	369.0	430.0	1173.0	308.0	143.0	1361.0	1104.0	814.0	879.0	1190.0
4	5	AMASYA	603.0	632.0	597.0	736.0	0.0	333.0	825.0	693.0	...	796.0	982.0	437.0	781.0	847.0	625.0	368.0	644.0	613.0	454.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
76	77	YALOVA	899.0	1152.0	338.0	1361.0	625.0	407.0	601.0	1254.0	...	1411.0	1562.0	371.0	1362.0	1472.0	0.0	350.0	1125.0	986.0	171.0
77	78	KARABÜK	714.0	960.0	447.0	1104.0	368.0	215.0	734.0	970.0	...	1164.0	1350.0	89.0	1078.0	1215.0	350.0	0.0	933.0	801.0	179.0
78	79	KİLİS	246.0	210.0	821.0	814.0	644.0	715.0	781.0	917.0	...	469.0	557.0	1001.0	924.0	917.0	1125.0	933.0	0.0	159.0	954.0
79	80	OSMANİYE	87.0	248.0	662.0	879.0	613.0	579.0	622.0	948.0	...	534.0	622.0	869.0	955.0	982.0	986.0	801.0	159.0	0.0	822.0
80	81	DÜZCE	735.0	981.0	375.0	1190.0	454.0	236.0	638.0	1083.0	...	1240.0	1391.0	200.0	1191.0	1301.0	171.0	179.0	954.0	822.0	0.0

	class	x1	x2	x3	x4	x5	x6	x7	x8	x9	...	x306	x307	x308	x309	x310	x311	x312	x313	x314	x315
0	6	-0.30400	-0.30400	-0.30400	-0.30400	-0.30400	-0.30400	-0.30400	-0.30400	-0.30400	...	-0.796	-0.742	-0.695	-0.648	-0.648	-0.6480	-0.6480	-0.6480	-0.6480	-0.6480
1	5	1.63000	1.63000	1.63000	1.63000	1.63000	1.63000	1.63000	1.63000	1.63000	...	-0.238	-0.238	-0.238	-0.238	-0.238	-0.2380	-0.2380	-0.2380	-0.2380	-0.2380
2	5	0.66100	0.66100	0.66100	0.66100	0.66100	0.66100	0.66100	0.66100	0.66100	...	-0.282	-0.237	-0.192	-0.147	-0.102	-0.0612	-0.0566	-0.0555	-0.0555	-0.0555
3	3	0.00518	0.00518	0.00518	0.00518	0.00518	0.00518	0.00518	0.00518	0.00518	...	1.210	1.150	1.090	1.060	1.050	1.0400	1.0200	0.9100	0.7910	0.6720
4	4	1.29000	1.29000	1.29000	1.29000	1.29000	1.29000	1.29000	1.29000	1.29000	...	-1.440	-1.440	-1.440	-1.440	-1.440	-1.4400	-1.4500	-1.4700	-1.4800	-1.5000

	class	y1	y2	y3	y4	y5	y6	y7	y8	y9	...	y306	y307	y308	y309	y310	y311	y312	y313	y314	y315
0	6	-2.120	-2.120	-2.120	-2.120	-2.120	-2.120	-2.120	-2.120	-2.120	...	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841	0.0841
1	5	0.667	0.667	0.667	0.667	0.667	0.667	0.667	0.667	0.667	...	-1.5200	-1.5500	-1.5900	-1.6300	-1.6600	-1.6600	-1.6600	-1.6600	-1.6600	-1.6600
2	5	-0.190	-0.190	-0.190	-0.190	-0.190	-0.190	-0.190	-0.190	-0.190	...	-1.4900	-1.4900	-1.4900	-1.4900	-1.4900	-1.4900	-1.4900	-1.4900	-1.4900	-1.4900
3	3	0.374	0.374	0.374	0.374	0.374	0.374	0.374	0.374	0.374	...	-1.5300	-1.6400	-1.7500	-1.8400	-1.9000	-1.9300	-1.9200	-1.6600	-1.3700	-1.0900
4	4	-0.397	-0.397	-0.397	-0.397	-0.397	-0.397	-0.397	-0.397	-0.397	...	-2.1800	-2.2200	-2.2400	-2.2400	-2.2400	-2.2300	-2.1900	-2.1300	-2.0700	-2.0100

	class	z1	z2	z3	z4	z5	z6	z7	z8	z9	...	z306	z307	z308	z309	z310	z311	z312	z313	z314	z315
0	6	-1.530	-1.530	-1.530	-1.530	-1.530	-1.530	-1.530	-1.530	-1.530	...	0.523	0.514	0.5030	0.4930	0.4750	0.456	0.438	0.419	0.401	0.382
1	5	1.790	1.790	1.790	1.790	1.790	1.790	1.790	1.790	1.790	...	-0.427	-0.427	-0.4270	-0.4270	-0.4290	-0.441	-0.453	-0.465	-0.477	-0.489
2	5	0.521	0.521	0.521	0.521	0.521	0.521	0.521	0.521	0.521	...	-0.863	-0.863	-0.8630	-0.8630	-0.8630	-0.863	-0.863	-0.863	-0.863	-0.863
3	3	0.309	0.309	0.309	0.309	0.309	0.309	0.309	0.309	0.309	...	-0.187	-0.124	-0.0559	0.0118	0.0795	0.157	0.254	0.446	0.649	0.852
4	4	-0.466	-0.466	-0.466	-0.466	-0.466	-0.466	-0.466	-0.466	-0.466	...	1.870	1.830	1.7600	1.6400	1.5200	1.450	1.520	1.630	1.750	1.870

IE582 HW2¶

Çiğdem Renkli¶

Task 1¶

Part a¶

Part b¶

Euclidian Distance¶

Manhattan Distance¶

Part c¶

Part d¶

Task 2¶

Task 3¶

Part a¶

Part b¶

	class	x1	x2	x3	x4	x5	x6	x7	x8	x9	...	z306	z307	z308	z309	z310	z311	z312	z313	z314	z315
0	6	-0.30400	-0.91200	-1.82400	-3.0400	-4.5600	-6.38400	-8.51200	-10.94400	-13.6800	...	-22366.665600	-22370.137100	-22373.105600	-22375.581100	-22377.581600	-22379.126100	-22380.232600	-22380.920100	-22381.206600	-22381.111100
1	5	1.63000	4.89000	9.78000	16.3000	24.4500	34.23000	45.64000	58.68000	73.3500	...	21494.078420	21497.799410	21501.093400	21503.960390	21506.398380	21508.395370	21509.939360	21511.018350	21511.620340	21511.733330
2	5	0.66100	1.98300	3.96600	6.6100	9.9150	13.88100	18.50800	23.79600	29.7450	...	23633.220000	23640.094800	23646.106600	23651.255400	23655.541200	23658.964000	23661.523800	23663.220600	23664.054400	23664.025200
3	3	0.00518	0.01554	0.03108	0.0518	0.0777	0.10878	0.14504	0.18648	0.2331	...	5379.666400	5377.270700	5374.819100	5372.379300	5370.019000	5367.815700	5365.866400	5364.363100	5363.508800	5363.506500
4	4	1.29000	3.87000	7.74000	12.9000	19.3500	27.09000	36.12000	46.44000	58.0500	...	-16915.639002	-16928.734347	-16940.069692	-16949.765037	-16957.940382	-16964.665727	-16969.871072	-16973.446417	-16975.271762	-16975.227107